Aizaz
Chatbots: conversational front doors
Website chat, WhatsApp replies, and FAQ bots fit when the job is inform, qualify, or route — not execute ten backend steps in production.
Use custom AI chatbot development when the primary goal is conversation, qualification, and handoff to a human or form — not updating ERP records autonomously.
Good chatbot use cases:
- Answering product or service questions from a knowledge base
- Qualifying inbound leads before CRM entry
- Routing support requests to the right queue
Agents: systems that do work
Agents call APIs, update CRMs, create tickets, and trigger workflows. Use them when the job is complete a process across tools — with logging, permissions, and human oversight.
See AI agent development for sales, support, and ops patterns where action — not just text — is required.
Good agent use cases:
- Creating or updating CRM records after a conversation
- Triggering fulfillment or exception workflows from structured intake
- Running multi-step internal processes with retries and alerts
Security and oversight
Both need logging, human handoff, and clear data boundaries. Agents need stricter tool permissions because they can change production data — the same bar we apply to LLM app development that touches customer or financial systems.
A practical sequence
Many teams start with a chatbot for qualification, then add agent capabilities once the conversation flow is stable and the integration points are understood.
If you are still prioritizing which workflow to automate first, start with how to identify workflows worth automating with AI — agents are rarely the right first step.
Conclusion
If users need answers, start with a chatbot. If your team needs work done across tools, build an agent — or both in sequence, not both on day one.